The present invention relates generally to the field of virtualization in cloud computing, and more particularly to workload management.
Current virtualization and cloud solution allow abstraction of the computing, storage, and network resources in terms of their capacity through standardization of the underlying system architecture to simplify the abstraction of these resources. On the other hand, the workload-optimized system approach relies on tight integration of the workload (including compiler) to the underlying system architecture. This approach allows direct leverage of the special capabilities offered by each micro-architecture and by the system level capabilities at the expense of required labor-intensive optimization and tuning.
A framework, referred to herein as the “Pfister framework,” has been used to describe workload characteristics of a given application. The Pfister framework considers “thread contention” versus “data contention.” With that in mind, four workload categories are defined: (i) mixed workload updating shared data or queues (such as enterprise software, also known as application and integration middleware); (ii) highly threaded applications; (iii) parallel data structures with analytics (such as frameworks for storage and large-scale processing of data sets on cluster computing environments); and (iv) small discrete applications.